Preprint WebVRGIS Based Traffic Analysis and

Visualization System

Xiaoming Li1,2,3, Zhihan Lv3,∗,Weixi Wang1,2, Baoyun Zhang4, Jinxing Hu3, Ling Yin3, Shengzhong Feng3

1. Shenzhen Research Center of Digital City Engineering, Shenzhen, China

2. Key Laboratory of Urban Land Resources Monitoring and Simulation,

Ministry of Land and Resources, Shenzhen, China

3. Shenzhen Institute of Advanced Technology(SIAT), Chinese Academy of Science, Shenzhen, China

4. Jining Institute of Advanced Technology Chinese Academy of Sicences, Jining, China

* Corresponding Author. Email: lvzhihan@gmail.com. Tel: +86 13791802272

Abstract

This is the preprint version of our paper on Advances in Engineering Software. With

several characteristics, such as large scale, diverse predictability and timeliness, the

city traffic data falls in the range of definition of Big Data. A Virtual Reality GIS

based traffic analysis and visualization system is proposed as a promising and inspiring

approach to manage and develop traffic big data. In addition to the basic GIS interac-

tion functions, the proposed system also includes some intelligent visual analysis and

forecasting functions. The passenger flow forecasting algorithm is introduced in detail.

Keywords: WebVRGIS; Virtual Traffic; Passenger Flow Forecasting; Virtual

Geographical Environment

1. Introduction

As an important part of urban transportation system, the urban passenger terminal

is an important joint-point for urban internal traffic and external traffic [1]. Integrat-

ing transportation routes including many highways and urban roads, multiple means

of transportation, it has necessary service function and control equipment, and also

has the comprehensive infrastructure which provides places for urban internal and ex-

ternal traffic transition and integrates traffic, commerce, and leisure. The urban pas-

senger terminal often gives a comprehensive consideration to urban external highway

passenger transportation and urban public traffic (track, bus, and taxi, etc.), private

transportation, as well as railway, aviation, and other external passenger transportation

Preprint submitted to Journal of LATEX Templates November 11, 2015

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to establish an organic passenger transportation and bring important benefits to urban

development [2].

With the evolution and outward extension of urban spatial structure, the lines of

public transportation are continuously expanded and extended; meanwhile, the means

of transportation has also been developed in a diversified way. The reasonable planning

and design and efficient management of urban passenger terminal are the important link

to improve urban public transportation system, solve residents’ transfer, and improve

the service quality and operation benefits of public transportation [3].

Nowadays, there is an increasing interest in using Virtual Reality Geographical

Information System (VRGIS), which can obtain the landscape geospatial data dynam-

ically, and perform rich visual 3D analysis, calculations, managements based on Geo-

graphical Information System (GIS) data [4] [5] [6] [7] [8]. As a medium composed of

interactive computer simulations that sense the participant’s position and actions and

replace or augment the feedback to one or more senses, virtual reality gives the feeling

of being mentally immersed or present in the simulation (a virtual world) [9]. With

several characteristics, such as large scale, diverse predictability and timeliness, traffic

data falls in the range of definition of Big Data [10]. In addition to the spatial data

integration, new user interfaces for geo-databases is also expected [11]. Therefore, the

management and development of traffic big data with virtual reality technology is a

promising and inspiring approach.

The urban comprehensive passenger transportation hub is a key node of the urban

transportation network. With various forms of traffic flows (for example, rail, bus,

taxi, long-distance passenger transportation) connected with the hub and complicated

forms of transfer among them, the passenger flow transportation efficiency will affect

the whole transportation network. In full consideration of the height of the entire hub,

the trend of information technology development and application and other aspects, it

is pretty necessary to research the real-time state information of the urban comprehen-

sive passenger transportation hub. First, for the daily management of transportation

hub, on one hand, the real-time publication of accessibility of all types of passenger

flows plays an important role in improving the hub’s service quality and optimizing the

whole transportation system; on the other hand, researching the residents’ long-term

3

Figure 1: The UI of the virtual reality geographic information system.

traveling needs and the spatial and temporal distribution of passenger flows can help

us to grasp the law of passenger flows and estimate the passenger flows, so as to take

administrative measures ahead of time. Second, for the planning of transportation hub

in Shenzhen, evaluating the service scope and serviceability of the existing transporta-

tion hubs is the significant basis for future planning and construction of transportation

hubs. Finally, for the technical level, the integration of diversified real-time dynamic

transportation information is the inevitable trend. The storage, analysis and 3D visu-

alization of the real-time dynamic transportation information for single nodes in the

transportation network and GIS-Transport and other technical trials help to provide

technical references for using the diversified real-time dynamic transportation infor-

mation during the integrated construction of municipal services. Some previous work

have inspired our work [12] [13].

2. System Division

Currently, there is a lack of integrated analysis and visual display of multiple real-

time dynamic traffic information, and also the lack of deep research and application

4

examples on this basis. This research takes Shenzhen Futian Comprehensive Trans-

portation Junction as the case, and makes use of continuous multiple real-time dy-

namic traffic information (currently using taxi data, the database also includes card

swiping data of public transport, and long-distance passenger traffic information, etc.)

to monitor out monitoring and analyze the on spatial and temporal distribution of pas-

senger flow under different means of transportation and service capacity of junction

from multi-dimensional space-time perspectives such as different period and special

period.

Virtual environments have proven to significantly improve public understanding of

3D planning data [14]. To share the information resources of all departments and the

dynamic tracking for the geospatial information of population and companies, an inte-

grated information system of social services is constructed. The use of virtual reality

as visual means has changed the traditional image of the city [15].

3D visualization of city building is to take inventory and display various types of

object data management and resource data within the community area which is so-

called virtual community [16], thus helping the social service agencies grasp the work

base with the support of holographic foundation library, such as population and com-

panies, houses, events and urban component and other factors. Based on rich Internet

application(RIA) technology [17], holographic data query for every household of the

specific community can be achieved respectively by building 3D virtual house and as-

sociating it with relevant data.

The geographic statistical analysis is to assist management decision-making and

conduct data analysis. 2D statistical analysis visualization is overlapped with a white

background on 3D virtual reality environment, since it’s more intuitive and a cogni-

tively less demanding display system, which lessens the cognitive workload of the

user [18]. The innovations this work include, real-time dynamic comprehensive trans-

portation data mining, 3D GIS analysis and release as for transportation junction; com-

prehensive assessment on service scope of junction by analyzing the long-term dy-

namic traffic data; comprehensive analysis on actual travel time and then comprehen-

sive assessment on service capacity of public transportation (accessibility and reliabil-

ity).

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Generally, the system can be divided into seven modules, respectively common tool

module, real-time traffic status module, dynamic traffic circle module, Origin and Des-

tination (OD)analysis module, passenger flow forecasting module, statistical analysis

module and bus transfer module, as shown in Figure 1. The proposed system is based

on WebVRGIS [19] and WebVR engine [20].

Basic functions: Switch the mouse into the dragging mode and realize the dragging

of 3D earth in any direction according to the mouse’s moving direction; then, click

to recover. Apart from map magnification and diminishing to display operations, it

is also feasible to realize the flight of the systematic map to the panorama location

displaying the map of Shenzhen through the panoramic function. The operating ways

of map sliding and rotating are also provided. The compass function can indicate the

current direction; if you click the compass button, the map will automatically calibrate

its direction to the due north direction upwards.

To state the property of road condition, text, picture or video labels can be added

on the surface or above the terrain. Besides, 2D lines, 2D faces, 3D models and vector

data can also be added on the land surface.

The real-time road condition function can load the information on road conditions

in real time and display it from the panoramic angle, as shown in Figure 2. The ac-

cessibility analysis function can analyze and display the accessible scope of Futian

Transportation Hub in the selected time in the panoramic form, as shown in Figure 3.

Transportation OD analysis: to analyze the number of vehicles and metro trains

from the hub to all regional centers according to the data of taxies and metro going in

and out of Futian Transportation Hub. The system will display the taxies from Futian

Hub to all the regional centers with rays with different degrees of thickness and display

the number of trains to all the metro stations at all the locations of metro stations with

different cylinders, shown on the left and right of Figure 4.

The passenger flow forecasting function: to analyze and display the daily and

monthly taxi passenger flows based on the historical statistical data and through the

passenger flow forecasting analysis method. The two different forms of bar graph and

curve graph are used to display the daily and monthly passenger flow volume by taxi

from Futian Hub, as displayed in Figure 5.

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Figure 2: The real-time road condition.

Figure 3: The accessibility analysis.

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Figure 4: Left: Transportation OD analysis for taxi; Right: Transportation OD analysis for subway.

Figure 5: Left: Passenger flow analysis by bar graph; Right: Passenger flow analysis by curve graph.

The bus transfer function is based on the bus route data in Shenzhen City and

combines the bus transfer algorithm. The bus transfer query and three-dimensional

transfer functions are provided. By entering the original station and terminal station

and clicking the Query button, the list will show the bus number to be taken; by clicking

the More button, the detailed transfer route will be displayed; by clicking the transfer

route, the system will display, on the 3D map, all the stations and the transfer stations

where the bus will pass by will appear, as shown in Figure 6.

3. Spatial Distribution and Forecast of Taxi Passenger Flow

The taxi passenger flow distribution can directly reflect the condition of ground

transportation system of a city at most, so it is a fast and effective way to solve the com-

plicated urban transportation problem by using the spatial and temporal distribution of

taxi passenger flow. OD spatial and temporal distribution, which is used to describe the

distribution of transportation volumes among all regions of a city and takes the traffic

8

Figure 6: Bus transfer function.

zone as its unit, is a significant component of traffic planning model. The division of

traffic zones is a good way to analyze the urban transportation network, because a traf-

fic zone has the similar traffic features and strong traffic relevance. The complete urban

transportation system is divided into a lot of traffic zones by the transportation flows,

road network layout and other features.

In this research the traffic zones are divided based on the distribution of road net-

works at all levels in Shenzhen, an OD matrix calculation is conducted according to the

taxi probe vehicle GPS data of Shenzhen to offer the OD spatial and temporal distribu-

tion of the taxies with passengers and deduce the OD spatial and temporal distribution

about transportations between the city and Futian Transportation Hub.

4. Analysis on Taxi Passenger Flow Volume

The data used in this forecast mainly include the real-time GPS data of vehicle.

The original data sheet mainly saves the data gathered by the GPS terminal installed

in the taxi, including license plate number, time point of data collection, longitude,

latitude, vehicle state, vehicle speed, driving direction, empty and load state, etc. Due

9

to the huge data volume every day, in consideration of such aspects as shortening the

data query time and improving the overall operational performance, we have sorted the

original data.

The following statistics in Figure 7 indicate the day-by-day variation in the number

of taxies entering and exiting from the station.

The horizontal axis represents the time, and the vertical axis represents the vehi-

cle flow volume. The data include those collected for 27 days in August, 14 days for

September and 24 days for October; the data for other days between August and Oc-

tober are absent. According to the analysis on the data collected for 65 days during

the three months, we find that the factors affecting the taxi flow volume are complex.

Among the statistics of taxi flow volume, the flow volumes of taxies entering and ex-

iting from the station are basically the same. By observing the data, it can be learned

that over 1500 vehicles entered the station on August 12, September 30 and October 1

respectively, and that over 1500 vehicles exited from the station on August 3, August

15 and October 1; according to the investigation, it was learned that such phenomenon

was caused by the 26th World University Games held in Shenzhen City from August

12 to August 23, in which period the traffic restriction, flow division at weekends and

other measures were implemented to avoid traffic congestion; therefore, the variation

trend of taxi flow volume in this period was not the same as usual; this was why a peak

flow volume of taxies entering the station occurred on August 12 when the opening

ceremony was held, and a peak flow volume of taxies exiting the station occurred on

August 15. A before-festival peak passenger flow volume occurred in the National Day

golden week from October 1 to October 7, and a valley formed quickly after the end

of the golden week. Excluding the two periods of time, no any obvious difference was

found in the passenger flow volumes of weekdays and weekends, indicating that the

crowds served by taxies are different from those served by other public transportations,

and that the trip purposes of passenger crowds are particular.

The analysis above indicates that there is no obvious variance for the taxi pas-

senger flow volume at Futian Transportation Hub Station has between weekdays and

weekends; but there is the rule that the flow volume entering the station is large in the

morning and the flow volume exiting the station is also large in the evening; indicating

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Figure 7: Flow Volume of Taxies Entering and Exiting the Futian Transportation Hub Station Between

August and October. Y-axis means the amount of the taxis.

that the main crowd served may be the long-distance passenger crowd; they start off in

the morning and come back in the evening by taking the long-distance bus.

5. Spatial Distribution of Taxi Passenger Flow

By adopting the regression forecast model, our research forecasts the passenger

flow of taxi. While predicting the passenger flow, there possibly many independent

variables affecting the result of dependent variable (passenger flow). As a matter of

fact, it is only permitted to find out some independent variables having important in-

fluence on the dependent variable, but neglect other independent variables. In concrete

application, it needs to screen out some major independent variables that may affect the

result of dependent variable, such a screening progress will apply the variance analy-

sis theory. In statistics, condition controlled in the experiment is factor. The state of

factor is processing or level. According to the experimental result is affected by one or

several factors, it can be divided into single-factor experiment and multi-factor exper-

iment. The sample of passenger flow varies at different stages every day. Therefore,

it can be concluded that date and time frame are two of many variable factors affect-

ing the passenger flow. By carrying out two-factor analysis of variance, it can get that

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whether these two factors are important one affecting the passenger flow, further to

consider them as input variables in the subsequent predictive analysis.

According to the transportation network, we divide the six districts of Shenzhen

City into 228 traffic zones; Futian District and Luohu District have the largest number

of traffic zones, because of their dense population, economic prosperity and compli-

cated industrial structures; the division of traffic zones can basically reflect the urban

population distribution, economic characteristics, industrial structures, traffic flow and

other features. Figure 8 left and Figure 8 right indicate the distribution of daily taxi

passenger sources and daily passenger flow respectively at Futian Transportation Hub

Station. These figures have revealed the major distribution conditions of the crowds

served by taxies; the passenger flow distribution is basically the same as the passenger

source distribution, indicating that the main crowds served by taxies are distributed in

those zones with dense pollution and prosperous economy. Besides, the range of taxi

service extends 9km towards the east and the west. According to the spatial and tem-

poral distribution characteristics of taxi passenger crowds, it is found that the crowds

arrive at Futian Transportation Hub Station by taxi with the main purpose of taking

the long-distance bus, and that the crowds taking taxies from the Futian Transportation

Hub Station arrived the station mainly by long-distance bus; the main foundation of a

taxi is for transferring between long-distance passenger flow and urban passenger flow.

6. Taxi passenger flow forecast

The passenger flow forecast refers to the index reflecting the demand characteris-

tics of traffic passenger flow by forecasting the cross sectional flow of urban transport

lines within certain period and inter-station OD. Upon planning the traffic network, the

passenger flow analysis result of different traffic network schemes is the main content

based on which the line network is selected.

People’s travel demand is the unity of randomness and regularity [21], and it is

reflected in large quantity of statistical data. Therefore, it is difficult to accurately

forecast the passenger flow in one day; however, the forecast can be made within certain

confidence, which is also the starting point of forecasting.

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Figure 8: Left: Distribution of Daily Taxi Passenger Flow Arriving at Futian; Right: Distribution of Daily

Taxi Passenger Flow Departing from Futian.

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There are many kinds of traffic passenger flow forecast models, and the common

models include regression forecast model and time series prediction model [22] [23].

In the observation period of this research, the passenger flow shows strong regularity

and stability without long-term change trend; therefore, regression forecasting model

is employed in this paper to forecast the passenger flow. While we forecast the passen-

ger flow, there may be many independent variables influencing the result of dependent

variable (passenger flow), but the actual situation is that it is only allowed to find out

several independent variables which have important influence on the dependent vari-

able and ignore other independent variables. In specific application, it is required to

screen out some main independent variables which influence the result of dependent

variable for research and analysis, and the analysis of variance theory is applied in this

screen-out process.

The analysis of variance is a kind of statistical analysis method in which the anal-

ysis and processing is made for significance of difference in mean values of some sets

of experimental data. The passenger flow samples are different in different periods

everyday; therefore, it is able to know that the date and period are 2 variables which

influence passenger flow; through dual-factor analysis of variance, it is able to deter-

mine whether these two factors are important factors which influence passenger flow

and then regard them as input variables in later forecasting analysis if so.

Due to difference in inbound and outbound passenger flow, this research carries out

forecasting modeling and model validation on inbound and outbound passenger flow

respectively.

For outbound traffic, predictive modeling and model validation contain the follow-

ing steps:

1. Outbound traffics of twenty-six days with normal data at each time period from

August to October are selected as modeling samples.

2. This study divides time period into 12 parts, and makes time period variable be-

come a categorical independent variable with 12 types, and thus 11 dependent variables

are generated, as follows:

Flow in = b1t1 + b2t2 + b3t3 + b4t4 + b5t5 + b6t6 + b7t7 + b8t8 + b9t9 + b10t10 +

b11t11 + b12 (1)

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Table 1: Regression modeling results of outbound traffics regression statistical results

Regression

Multiple R 0.906573

R Square 0.821875

Adjusted R Square 0.815321

Standard Error 22.81944

Observed Value 311

Table 2: Regression modeling results of outbound traffics variance analysis results

df SS MS F Significance F

Regression

Analysis

Residual

Error

Total

11

718390.5

65308.23

125.4175

5.1E-105

299 155697.3 520.7268

310 874087.8

Where, t1,t2, . . .t11 are dependent variables of 11 time periods. When t1 = 1,t2 =

t3 = . . . = t11 = 0, dependent variables denote outbound traffics of the first time period;

when t2 = 1,t1 = t3 = . . . = t11 = 0, dependent variables denote outbound traffics of the

second time period; reason by analogy; when t1 = t2 = t3 = . . . = t11 = 0, dependent

variables denote outbound traffics of the twelve time period. bi is independent variable

parameters and constant terms of these four models respectively.

3. By modeling with sample data in Table 1, results of predictive model of outbound

taxi are as follows:

According to Table 1, predictive model of outbound traffics has Adjusted R Square=81.53%,

i.e. this model can explain 81.53% of sample data; the entire model has statistical sig-

nificance at level α = 0.05; all parameters of the model have p-value smaller than 0.05,

which means that all of these parameters have statistical significance at level α = 0.05.

To sum up, predictive models of outbound traffics have good imitative effects of

sample data and are expressed as follows:

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Table 3: Regression modeling results of outbound traffics model coefficient results

Coefficients Standard Error t Stat P-value

Intercept 54.07692 4.47526 12.08353 1.21E-27

1 -28.4369 6.39195 -4.44887 1.22E-05

0 -46.2308 6.328973 -7.30462 2.54E-12

0 -21.8462 6.328973 -3.45177 0.000637

0 2.384615 6.328973 0.376778 0.706606

0 36.11538 6.328973 5.706357 2.78E-08

0 65.73077 6.328973 10.38569 8.88E-22

0 69.07692 6.328973 10.9144 1.45E-23

0 82.57692 6.328973 13.04744 4.18E-31

0 98.38462 6.328973 15.54511 2.39E-40

0 89.61538 6.328973 14.15955 3.49E-35

0 52.26923 6.328973 8.258722 4.81E-15

Table 4: Validation results of predictive model of taxi outbound traffics at different time periods

Maximum Error Minimum Error Mean Error

125.32% 1.23% 23.46%

Flow out = −28.44t1 − 46.23t2 − 21.85t3 + 2.38t4 + 36.16t5 + 65.73t6 + 69.08t7 +

82.58t8 + 98.38t9 + 89.62t10 + 52.27t11 + 54.08 (2)

Outbound traffics at time periods with normal data within two days of October are

selected as model validation samples. Validate prediction formula with validation sam- ples

and calculate the accuracy of each independent predicted value with mean absolute

percentage error (MAPE): MAPE = 100%×∥predictedvalue−actualvalue∥/actualvalue.

Validation results are as Table 4.

As can be known from Table 3, the mean error of predictive model of taxi outbound

traffics at different time periods is 23.46%, and thus this model has good validation

effects.

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7. Conclusion

In this research, Futian Transportation Junction is taken as research objective, and

the spatial and temporal distribution rules of passenger flow and service scope of junc-

tion of all means of transportation are found by carrying out statistical analysis on

long-term daily passenger flow and daily time-phased passenger flow in Futian Trans-

portation Junction according to the data such as Shenzhen TransCard data, taxi and

floating car data, and long-distance passenger transportation data, and a short-term

forecast is made on various kinds of passenger flow. Furthermore, the key analysis is

made on abnormal traffic condition in the period of Universiade and National Day hol-

idays. It is found from the research that Futian Transportation Junction currently has

good operation condition, and the passenger flow of various means of transportation

doesn’t reach the designed upper limit of passenger flow even in peak of passenger

flow in holidays, without obvious transfer and congestion. Within short term, various

means of transportation have stable passenger flow, and the time-phased passenger pre-

diction model is reliable. The main service group of junction is distributed in west of

Nanshan District and Futian District, as well as west of Luohu District and the scope

which radiates about 9km based on the junction.

Then, the travel time is used as index to carry out a series of researches and analyses

on accessibility of public transportation of Futian Transportation Junction. It is found

from the research that the junction has good accessibility. Then, the actual fluctuation

of travel time is taken as index to analyze the reliability of junction network. It is found

that the overall reliability is high, and there is only poor reliability at the peak travel

time at Huaqiang North station and Bao’an center station; as for some business centers

and hot working area, there is poor reliability at the peak travel time; as for places with

strong entertainment such as Overseas Chinese Town and Honey Lake, there is a poor

reliability at travel time in holidays.

In addition, the 3D case of Shenzhen proves that 3D city visualization and analysis

system is a useful tool for the social service agencies and citizens for browsing and

analyzing city big data directly, and is agreed upon as being both immediately useful

and generally extensible for future applications.

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8. Future Work

Through long-term monitoring and analysis, the long-term passenger flow forecast

is established under the condition of combing with economic development and urban

planning. Our future analysis will be made on population travel behavior, taxi route,

degree of influence on public bus, and road travelling speed under special weather

conditions (such as rainstorm, typhoon, and heavy fog). The deeper data mining

will be made, such as emergency evacuation aided decision support, monitoring and

forecasting on large-scale group event, assisting crowd and vehicle evacuation under

emergency. In addition to city, ocean data will be integrated into the proposed sys-

tem [24] [25], in which we will employ spatiotemporal visualization as the representa-

tive approach [26] [27]. Some novel human-computer-interaction approaches [28] [29] [30] [31] [32] [33],

enhanced vision feedback technologies [34] [35] [36] [37] [38], network technolo-

gies [39] [40] [41] [42], bigdata [43] [44] [45] [46] and some optimization approaches [47] [48] [49] [50] [51]

are considered to be integrated in our future work.

Acknowledgments

The authors are thankful to the National Natural Science Fund for the Youth of

China (41301439), LIESMARS Open Found(11I01) , Shenzhen Scientific & Research

Development Fund (JC201105190951A, JC201005270331A, CXZZ20130321092415392),

and Electricity 863 project(SS2015AA050201).

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